Dr. Bowei Chen from University of Glasgow (UK)g Probabilistic Modelling and Machine Learning for Online Advertising.Online advertising has become a significant source of revenue for web-based businesses. In this talk, I will introduce several of our recent studies which develop probabilistic models and machine learning algorithms to improve the current advertising delivery system and sales model. In the first study, we propose an optimal dynamic model for online publishers which can unify programmatic guarantee and real-time bidding in display advertising. The model solves the problem of algorithmic pricing and allocation of the non-guaranteed page views into guaranteed contracts with stochastic demand. In the second study, we develop an average price advertising options which can provide flexible guaranteed delivery to advertisers. Jump-diffusion processes are used to model the evolution of the spot market prices from advertising slots. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option pay-off is based on the geometric mean. This pricing formula is also a generalised version of several other option pricing models discussed in related studies. In the third study, we develop a novel computational framework which brings multimedia metrics like contextual relevance, visual saliency and advertisement memorability into real-time bidding. It aims to improve online user's experience as well as maintain the benefits of online publisher and advertiser in display advertising.Bowei Chen is an Assistant Professor at the Adam Smith Business School of University of Glasgow. He received a PhD in Computer Science from University College London and works in the cross sections among data science, machine learning and business studies, with special focus on digital marketing and quantitative finance. He was an Assistant Professor in the School of Computer Science at University of Lincoln, and has held visiting scholar and professional positions at Université de Technologie Belfort-Montbéliard, Copenhagen Business School, Aarhus University, University of Bath, and National University of Singapore.

Dr. Serge Iovleff from university of LilleCo-Clustering of Binary Data with Gaussian Co-variablesThe simultaneous grouping of rows and columns is an important technique that is increasingly used in large-scale data analysis. In this talk, I present a novel co-clustering method using co-variables in its construction. It is based on a latent block model taking into account the problem of grouping variables and clustering individuals by integrating information given by sets of co-variables. Numerical experiments on simulated data sets and an application on real genetic data highlight the interest of this approach.Slides Part 1PDF, Slides Part 2PDF

2018

Date

Hour

Room

Details

November 28th

11.00

M208, Montbéliard

Pr. H. Aoki from university of Nagoya (Japan)Autonomous driving technologies and applicationsSlidesPDF

November 13th

16.15

A206, Belfort

Dr. Tomas KrajnikFreMEn: Frequency Map Enhancement for Long-Term Autonomy of Mobile RobotsWhile robotic mapping of static environments has been widely studied, life-long mapping in non-stationary environments is still an open problem. We present an approach for long-term representation of natural environments, where many of the observed changes are caused by pseudo-periodic factors, such as seasonal variations, or humans performing their daily chores. Rather than using a fixed probability value, our method models the uncertainty of the elementary environment states by their frequency spectra. This allows to integrate sparse and irregular observations obtained during long-term deployments of mobile robots into memory-efficient models that reflect the recurring patterns of activity in the environment. The frequency-enhanced spatio-temporal models allow to predict the future environment states, which improves the efficiency of mobile robot operation in changing environments. In a series of experiments performed over periods of weeks to years, we demonstrate that the proposed approach improves mobile robot localization, path and task planning, activity recognition and allows for life-long spatio-temporal exploration.

September 27th

14.30

Meeting Room, Building D, Belfort

Pr. Nidal KamelSVD-Based Tensor-Completion Technique for Background InitializationExtracting the background from a video in the presence of various moving patterns is the focus of several background-initialization approaches. To model the scene background using rank-one matrices, this paper proposes a background-initialization technique that relies on the singular value decomposition (SVD) of spatiotemporally extracted slices from the video tensor. The proposed method is referred to as spatiotemporal slice-based SVD (SS-SVD). To determine the SVD components that best model the background, a depth analysis of the computation of the left/right singular vectors and singular values is performed, and the relationship with tensor-tube fibers is determined. The analysis proves that a rank-1 matrix extracted from the first left and right singular vectors and singular value represents an efficient model of the scene background. The performance of the proposed SS-SVD method is evaluated using 93 complex video sequences of different challenges, and the method is compared with state-of-the- art tensor/matrix completion-based methods, statistical-based methods, search-based methods, and labeling-based methods. The results not only show better performance over most of the tested challenges, but also demonstrate the capability of the proposed technique to solve the background-initialization problem in a less computational time and with fewer frames.Pr. Nidal Kamel received the M.Sc and PhD degree (Hons) from the Technical University of Gdansk, Poland, in 1993. His PhD work was focused on subspace-based array signal processing for direction of-arrival estimation. Since 1993 he has been involved in research projects related to estimation theory, noise reduction, optimal filtering, and pattern recognition. He developed SNR estimator for antenna diversity combining, single-trial subspace-based technique for EEG extraction form brain background noise, and introduced a subspace-based data glove system for online signature verification. His present research interest is in brain signal processing, image enhancement, and pattern recognition. Currently, he is Associate Professor at the PETRONAS University of Technology. He is IEEE senior member.

Dr. You LiLiDAR and Its Trend in Automotive ApplicationIn this talk, Dr. Li will present his work at Renault Innovation, including principle of LiDAR, different types of LiDAR, usages for autonomous driving (e.g. object description and localization), and especially, challenges for automotive usage and its trend in the future.

May 3rd

14.00

Meeting Room, Building D, Belfort

Hui ZHAOAgent-based Dynamic Rescheduling of Daily ActivitiesWhen simulating individuals’ daily plan, in order to determine the effect on the road network, lots of unexpected events need to be considered, like traffic jam and weather changes. Therefore, there will be a mismatch between the original plan and the executed one. Faced with this situation, individuals need to adjust the rest of the activities to make a new schedule. This paper analyzes the causes of rescheduling, and establishes a new rescheduling model, combining strengths of existing rescheduling models. The model in this paper considers the rescheduling possibilities and choices as much as possible. It takes time pressure and schedule similarity into consideration when updating a schedule. Furthermore, this paper analyzes joint trip/activity execution by studying the cooperation between agents during the rescheduling process.This talk will be given to the FAMS18 conference.

April 16th

9:30

Meeting Room, Building D, Belfort

Dr. Li Sun - Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, UKComputer vision and machine learning for robotics applicationsIn this talk, Dr. Li will present his work for three European projects. Dr. Li Sun's PhD was working for EU FP7 CloPeMa project (http://www.clopema.eu/), aiming to achieve autonomous robot laundering. In this project, he was working on visually-guided dual-arm manipulation of deformable clothes. He was the main contributor of two robot demonstrations, i.e. dual-arm clothes on-table flattening, autonomous clothes categorization, and interactive sorting. In his research, a generic computer vision architecture using stereo robot head is proposed for multiple-laundry tasks including grasping, recognition, flattening, and sorting. Latter, he was working for EU H2020 RoMaNs project (https://www.h2020romans.eu) and then EU H2020 ILIAD project (https://iliad-project.eu), focusing on deep learning based table-top object detection and long-term semantic mapping of the indoor and outdoor scene.

March 29th

14.00

Meeting Room, Building D, Belfort

Yazan MUALLAComparison of Agent-based Simulation Frameworks for Unmanned Aerial Transportation ApplicationsRecently, the applications of Unmanned Aerial Vehicles (UAVs) in aerial transportation are gaining more interest. Due to operational costs, safety concerns and legal regulations, agent-based simulation frameworks are preferably used to implement models and conduct tests. With the abundance of such frameworks, this paper introduces a methodology to compare the most widely used frameworks. The methodology is based on the ISO software quality model, and uses a weighted sum scoring system to give points to the frameworks under study. The proposed criteria in the methodology consider agent-based simulation features and adapt specific features of unmanned aerial transportation. Preliminary comparison results and recommendations are provided

and discussed.This talk will be given to the ABMTRANS18 conference.

2017

Date

Hour

Room

Details

September 28th

14.00

Meeting Room, Building D, Belfort

Fabrice LAURIDeep Reinforcement LearningIn 2013, a small company in London called DeepMind uploaded their pioneering paper “Playing Atari with Deep Reinforcement Learning” to Arxiv. In this paper they demonstrated how a computer learned to play Atari 2600 video games by observing just the screen pixels and receiving a reward when the game score increased. The result was remarkable, because the same model architecture, without any change, was used to learn how to win in seven different games, and in three of them the algorithm performed even better than a human!

No wonder DeepMind was immediately bought by Google and has been on the forefront of deep learning research ever since.
It has been hailed since then as the first step towards general artificial intelligence – an AI that can survive in a variety of environments.
The roadmap of this seminar is:
- What are the main challenges in reinforcement learning?
- How to formalize reinforcement learning in mathematical terms?
- How do we form long-term strategies?
- How can we estimate or approximate the future reward?
- What if our state space is too big? (Here the answer is simple: deep learning!)
- What are the main deep RL algorithms?
- What performances can these algorithms obtain on classical problems?
- What are the main issues of applying such algorithms?
- How to program such algorithms and validate them on common problems with Ipseity?SlidesPDF

Dr. Benjamin CamusDEVS wrapping of the FMI standard for the co-simulation of Cyber-Physical Systems in MECSYCOMost modeling and simulation (M&S) questions about cyber-physical systems (CPS) require expert skills belonging to different scientific fields. The challenges are then to integrate each domain tool (formalism and simulation software) within the rigorous framework of M&S process. To answer this issue, we proposed the specifications of the MECSYCO co-simulation middleware. MECSYCO relies on the universality of the DEVS formalism to integrate models written in different formalisms. This integration is based on a wrapping strategy in order to make models implemented with different simulation software interoperable. So far, we successfully defined DEVS wrappers for discrete modeling tools like the MAS simulator NetLogo, and the IP network simulators NS-3 and OMNeT++/INET. Aside from several difficulties met at the software level, making these discrete modeling tools compliant with the DEVS simulation protocol was a straightforward process. This is due to the fact that these platforms have a discrete modeling paradigm very close to DEVS. However, things getting more complex with equation-based tools as their continuous modeling paradigm is very different from the discrete DEVS one. Thus, we need to bridge the gap between the discrete and the continuous paradigms. A more complex wrapping strategy based on the hybrid capacity of DEVS is required. Regarding this issue, wrapping each of these equation-based tools (e.g. OpenModelica, Dymola, Matlab/Simulink) separately would be very inefficient. In this talk, I will detail how we tackle this issue by defining DEVS wrappers for the FMI standard which brings a generic API to manipulate equation-based models and their solvers. We perform this wrapping using the DEV&DESS hybrid formalism and the QSS numerical method. The DEVS wrapping of FMI we propose is not restricted to MECSYCO but can be performed in any DEVS-based platform.SlidesPDF‎

June 29th

14.00

Belfort

Pr. Ansar YasarEmpowering Citizens with Sustainable Transportation in the Cities of Today & TomorrowWhile some may argue that the added value of one research domain is more limited in terms of added economic value than the other, the contribution of transportation research towards the society as a whole is significant. According to several predictions, the transport sector will overtake industry as the largest energy user by 2020. Unfortunately, the sector has major negative economic, social and environmental side effects. The complexity of today’s policy decision making has motivated several international research teams to develop policy frameworks which are finally aimed at mitigating these negative externalities of transport.

In several international policy frameworks, conventional transport models have been used for the quantification of these externalities. When an operational model is required to provide quantitative predictions about human behaviour, some kind of mathematical apparatus is adopted in models. In this talk, we will cover the research domain of activity-based models. In these models, using micro-simulation, full activity-travel patterns of people are predicted in a high resolution of time and space, offering a wealth of information for policy making. The models give us a behavioural insight at an unprecedented level and allow for many interesting interdisciplinary applications. In this talk, I will give a brief overview of the state-of-the-art in activity-based modelling and discuss the interesting developments in this field of research. In addition to applications in the domain of transportation research, I will focus on scientific interdisciplinary applications with several other scientific fields, such as emission and health impact calculations, traffic safety and future electric vehicle (market) projections. Also the talk will cover novel interesting trends in the research field such as the increasing availability of big data and the development of modern survey technology, which offers several opportunities for policy makers but also provides researchers with novel challenges and problems.SlidesPDF

June 1th

14.00

Meeting Room, Building D, Belfort

Dr. Vukosi MarivateMachine Learning, Reinforcement Learning

June 1th

10.30

Meeting Room, Building D, Belfort

Dr. Nidal KamelSubspace-Based Estimators for Image DenoisingDigital images are susceptible to various types of noise that may which affects their quality. In the field of image enhancement, different approaches for noise reduction have been proposed. In general, there are two basic approaches to image denoising, spatial filtering methods and transform domain filtering methods. Spatial filtering methods include linear methods like the mean and Winer and nonlinear methods, like the median and the weighted median. The performance of these ﬁlters is highly dependent on the choice of size and orientation of the moving window. Transform domain filtering methods are mostly dominated by the Wavelet, where the image is first transformed into the wavelet domain then a thresholding scheme is applied. The major drawback of the wavelet-based technique is the ringing impairments due to the thresholding process. Recently, the area of the subspace based filters, has gained widespread attention and successfully implemented in various areas of image densoing. In this lecture, two subspace-based techniques to reduce the noise in images are outlined. These techniques are the Least Squares Estimator, and the Time Domain Constraints Estimator (TDC).

May 4th

14.00

Meeting Room, Building D, Belfort

Pr. Vincent ChevrierMecsyco: Multi-agent Environment for the Co-simulation of COmplex systemsMost modeling and simulation (M&S) questions about complex systems require to take simultaneously account of several points of view. Phenomena evolving at different scales and at different levels of resolution have to be considered. Moreover, expert skills belonging to different scientific fields are needed. The challenges are then to reconcile these heterogeneous points of view, and to integrate each domain tools (formalisms and simulation software) within the rigorous framework of the M&S process.

This talk will present the mecsyco co-simulation middleware (mecsyco.com). Mecsyco r elies on the universality of the DEVS formalism to integrate models written in different formalism. This integration is based on a wrapping strategy in order to make models implemented in different simulation software inter-operable. The middleware performs the co-simulation in a parallel, decentralized and distributable fashion thanks to its modular multi-agent architecture.SlidesPDF

SARL is a general-purpose agent-oriented language. It aims at providing the fundamental abstractions for dealing with concurrency, distribution, interaction, decentralization, reactivity, autonomy and dynamic reconfiguration. These high-level features are now considered as the major requirements for an easy and practical implementation of modern complex software applications, and specifically agent-oriented programming.
This talk will introduce you to the advances of the SARL agent programming languages, and provides several examples of usages.